Abstract
Current advanced deep neural networks can greatly improve the performance of emotion recognition tasks in affective Brain-Computer Interfaces (aBCI). Basic human emotions could be induced and electroencephalographic (EEG) signals could be simultaneously recorded. While data of basic common emotions are easier to collect, some complex emotions are low resource in terms of data size and label quality in real life, which would limit the utility of EEG-based emotion recognition models. To enhance the model adaptive capacity of new emotions with few samples, we introduce a few-shot class-incremental deep learning model for emotion recognition. The proposed model consists of a graph convolutional networks (GCN) and a linear classifier. By training the whole network on a base set in a preliminary stage, and fine-tuning the parameters of the linear classifier with very few shots of labeled samples, the model can incrementally learn new types of emotions while preserving knowledge of the old ones. Our experimental results on the SEED-V dataset show that even with very limited new class samples, the fine-tuned pre-trained model could have a fairly good performance on the test set with more emotion classes.
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References
Alarcao, S.M., Fonseca, M.J.: Emotions recognition using EEG signals: a survey. IEEE Trans. Affect. Comput. 10(3), 374–393 (2017)
Akyürek, A.F., Akyürek, E., Wijaya, D., Andreas, J.: Subspace regularizers for few-shot class incremental learning. arXiv preprint arXiv:2110.07059 (2021)
Dhillon, G.S., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. arXiv preprint arXiv:1909.02729 (2019)
Dong, S., Hong, X., Tao, X., Chang, X., Wei, X., Gong, Y.: Few-shot class-incremental learning via relation knowledge distillation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 2, pp. 1255–1263 (2021)
Ekman, P.: An argument for basic emotions. Cogn. Emotion 6(3–4), 169–200 (1992)
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems, vol. 17 (2004)
Jacob, L., Vert, J.P., Bach, F.: Clustered multi-task learning: a convex formulation. In: Advances in Neural Information Processing Systems, vol. 21 (2008)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521–3526 (2017)
Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935–2947 (2017)
Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001–2010 (2017)
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)
Schonfeld, E., Ebrahimi, S., Sinha, S., Darrell, T., Akata, Z.: Generalized zero-shot learning via aligned variational autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 54–57 (2019)
Shi, L.C., Lu, B.L.: Off-line and on-line vigilance estimation based on linear dynamical system and manifold learning. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 6587–6590. IEEE (2010)
Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.: Few-shot class-incremental learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12183–12192 (2020)
Zhao, L.M., Li, R., Zheng, W.L., Lu, B.L.: Classification of five emotions from EEG and eye movement signals: complementary representation properties. In: 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 611–614. IEEE (2019)
Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162–175 (2015)
Zheng, W.L., Zhu, J.Y., Lu, B.L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput. 10(3), 417–429 (2017)
Zheng, W.L., Liu, W., Lu, Y., Lu, B.L., Cichocki, A.: Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Trans. Cybern. 49(3), 1110–1122 (2018)
Acknowledgements
This work was supported in part by grants from the National Natural Science Foundation of China (No. 61976135), MOST 2030 Brain Project (No. 2022ZD0208500), Shanghai Municipal Science and Technology Major Project (No. 2021SHZDZX), SJTU Global Strategic Partnership Fund (2021 SJTUHKUST), Shanghai Marine Equipment Foresight Technology Research Institute 2022 Fund (No. GC3270001/012), and GuangCi Professorship Program of RuiJin Hospital Shanghai Jiao Tong University School of Medicine.
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Ma, TF., Zheng, WL., Lu, BL. (2023). Few-Shot Class-Incremental Learning for EEG-Based Emotion Recognition. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_38
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DOI: https://doi.org/10.1007/978-981-99-1642-9_38
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